In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method o...

This is a difficult because: 1. Object appearance varies 2. Way of interacting with object vary among people 3. Both object and body parts of the interest might be occluded Contributions By using motion information alone, we propose an unsupervised framework for clustering and classifying videos of people interacting with objects. The method is based on [2]. We show that: 1. human-object intera...

A challenge involved in applying density-based clustering to categorical datasets is that the ‘cube’ of attribute values has no ordering defined. We propose the HIERDENC algorithm for hierarchical densitybased clustering of categorical data. HIERDENC offers a basis for designing simpler clustering algorithms that balance the tradeoff of accuracy and speed. The characteristics of HIERDENC includ...

Subspace clustering (also called projected clustering) addresses the problem that different sets of attributes may be relevant for different clusters in high dimensional feature spaces. In this paper, we propose the algorithm DiSH (Detecting Subspace cluster Hierarchies) that improves in the following points over existing approaches: First, DiSH can detect clusters in subspaces of significantly...

We establish the convergence of the fuzzy subspace clustering (FSC) algorithm by applying Zangwill’s convergence theorem. We show that the iteration sequence produced by the FSC algorithm terminates at a point in the solution set S or there is a subsequence converging to a point in S. In addition, we present experimental results that illustrate the convergence properties of the FSC algorithm in...

We propose a transformation method to circumvent the problems with high dimensional data. For each object in the data, we create an itemset of the k-nearest neighbors of that object, not just for one of the dimensions, but for many views of the data. On the resulting collection of sets, we can mine frequent itemsets; that is, sets of points that are frequently seen together in some of the views...

The first step in modeling any physical phenomena is the identification of the relevant variables, and then relating these variables via known physical laws. For sufficiently simple phenomena we can usually construct a quantitative relationship among these variables from first principles; however, for many complex phenomena (which often occur in engineering applications) such an ab initio theor...

acrylic resin denture bases undergo dimensional changes during polymerization. injection molding techniques are reported to reduce these changes and thereby improve physical properties of denture bases. the aim of this study was to compare dimensional changes of specimens processed by conventional and injection-molding techniques.sr-ivocap triplex hot resin was used for conventional pressure-pa...

Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed i...

Low rank representation (LRR) has recently attracted great interest due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. One of its successful applications is subspace clustering, by which data are clustered according to the subspaces they belong to. In this paper, at a higher level, we intend to cluster subspaces into classes of subspaces. This is nat...